According to a prediction last year by Deloitte, by the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products.” Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the enterprise, and AI developments can create value.

This value can be captured/visualized by considering an ‘Enterprise AI layer.’ This AI layer is focussed on solving relatively mundane problems that are domain-specific. While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

In this brief article, we proposed a logical concept called the AI layer for the Enterprise. We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate IoT datasets and unite the disparate ecosystem.

Enterprise AI: An Intelligent Data Warehouse/ERP System?

AI enables computers to do some things better than humans, especially when it comes to finding insights from large amounts of unstructured or semi-structured data. Technologies like machine learning, natural language processing (NLP), speech recognition, and computer vision drive the AI layer. More specifically, AI applies an algorithm, which is learning on its own.

To understand this, we have to ask ourselves: How do we train a Big Data algorithm?

There are two ways:

Start with the rules and apply them to data (top-down).

Start with the data and find the rules from the data (bottom-up)

The top-down approach involved writing enough rules for all possible circumstances. But this approach is obviously limited by the number of rules and by its finite rules base. The bottom-up approach applies for two cases. Firstly, when rules can be derived from instances of positive and negative examples (SPAM/NO SPAM). This is traditional machine learning, when the algorithm can be trained. But the more extreme case is where there are no examples to train the algorithm.

What do we mean by ‘no examples’?

There is no schema.

Linearity (sequence) and hierarchy is not known.

The output is not known (non-deterministic).

Problem domain is not finite.

Hence, this is not an easy problem to solve. However, there is a payoff in the enterprise if AI algorithms can be created to learn and self-train manual, repetitive tasks – especially when the tasks involve both structured and unstructured data.

How can we visualize the AI layer?

One simple way is to think of it as an ‘intelligent data warehouse,’ i.e. an extension to either the data warehouse or the ERP system.

For instance, an organization would transcribe call center agents’ interactions with customers to create a more intelligent workflow, bot, etc. using deep learning algorithms.

The Enterprise AI Layer: What It Means to the Enterprise

So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered? Here are some examples

Bots: Bots are a great example of the use of AI to automate repetitive tasks, like scheduling meetings. Bots are often the starting point of engagement for AI, especially in retail and financial services

Inferring from textual/voice narrative: Security applications to detect suspicious behavior, algorithms that can draw connections between how patients describe their symptoms, etc.

These applications provide a competitive advantage: differentiation, customer loyalty, and mass personalization. They have simple business models (such as being deployed as premium features/new products/cost reduction).

The Enterprise AI Layer and IoT

So, the final question is: What does the enterprise layer mean for IoT?

IoT has tremendous potential but faces an inherent problem. Currently, IoT is implemented in verticals/silos, and these silos do not talk to each other. To realize the full potential of IoT, an over-arching layer above individual verticals could ‘connect the dots.’ Coming from the Telco industry, these ideas are not new, i.e. the winners of the mobile/Telco ecosystem were iPhone and Android – which succeeded in doing exactly that.

Firstly, the AI layer could help in deriving actionable insights from billions of data points, which come from IoT devices across verticals. This is the obvious benefit, as IoT data from various verticals can act as an input to the AI layer. Deep learning algorithms play an important role in IoT analytics because machine data is sparse and/or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for the data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms to learn on their own.

We can extend the idea of ‘machines teaching other machines’ more generically within the enterprise. Any entity in an enterprise can train other ‘peer’ entities in the enterprise. That could be buildings learning from other buildings – or planes or oil rigs. We see early examples of this approach in Salesforce.com and Einstein. Longer term, reinforcement learning is the key technology that drives IoT and AI layer for the enterprise – but initially, any technologies that implement self-learning algorithms would help for this task

Conclusion

In this brief article, we proposed a logical concept called the AI layer for the enterprise. We could see such a layer as an extension to the data warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem. This will not be easy, but it is worth it because the payoffs for creating such an AI layer around the enterprise are huge!